The ever-increasing use of online news feeds, social media platforms, and other online content services has resulted in substantial demand for new and interesting content. In addition, today's 24/7 news cycle requires the accurate generation and quick delivery of relevant, topical articles in response to news events. The amount of human capital required to satisfy these demands is significant. Journalists, fact-checkers and other copy writers must work quickly, efficiently and in large numbers to generate meaningful content around the clock.
To remedy this, some semi-automated natural language generation systems have been created. However, to date these systems have been limited to basic fill-in-the-blank templates, which still require human completion and which have little ability to create interesting and varied content.
In addition, existing natural language generation systems are limited in their ability to receive and integrate real-time news from a variety of content publishers. With the explosion of digital content feeds, the processing power and bandwidth required to quickly identify information that can be used to generate content can require an enormous cost, and it generally limits natural language generations to using an existing (and static) corpus of source material. In addition, existing systems cannot generate dynamic content that changes in real-time as facts change and/or source material is updated. Further, the proliferation of “fake news” and propaganda-laden feeds creates additional technical challenges for automated systems to be able to discern reliable source material from false or misleading information.
This document describes methods and systems that are directed to solving at least some of the issues described above.